List of new tutorials

In order to keep you informed on new tutorials, I’ll post updates in here.

The following tutorials were already added in 2020:

Feel free to test them and comment in here! Any feedback is appreciated.


The latest addition is Landcover classification with Sentinel-1 GRD

It contains

  • pre-processing of Sentinel-1 GRD data
  • the generation of image textures
  • principal component analysis
  • unsupervised clustering
  • rule-based classification using the Mask Manager
  • supervised classifications based in training data
  • assessment of the training and prediction accuracy

@ABraun Thank you fore the great job done!
Nice material for students and practitioners.

May I give you a suggestion for one of the next tutorials - make a summary of StaMPS thread, if possible.

thank you @hriston_bg
We considered a separate tutorial on StaMPS but have not decided for one because of two reasons:

  • there are two very detailled instructions available here and here and I could not make it any better
  • Large parts are done in StaMPS which was not developed by ESA, so we leave the documentation and support to those in charge.

Still, we collect all developments here: StaMPS - Detailled instructions to provide an easy start for anyone interested.

Sorry, but the tutorial of " Synergetic use of S1 (SAR) and S2 (optical) data and use of analysis tools" can not be opened, the URL gives an error. Thanks!

thank you for reporting - I updated the url.

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Hello, @ABraun! I am working with s1 time-series data. I read “Time-series analysis with Sentinel-1” and I noticed that you do not filter the images when you are pre-processing, instead you apply a multitemporal filter to the whole time-series images. Why didn’t you filter them before? I am a bit confused about how to do it.

both is legit, I would say. The multi-temporal filter greates more smooth gradients between the dates and is considered more precise regarding speckle, while the single product flter will preserve the information of each product a little better.

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Great, thanks!